Papers › Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained...
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition
Dichao Liu
Fine-grained vehicle recognition (FGVR) is an essential fundamental technology for intelligent transportation systems, but very difficult because of its inherent intra-class variation. Most previous FGVR studies only focus on the intra-class variation caused by different shooting angles, positions, etc., while the intra-class variation caused by image noise has received little attention. This paper proposes a progressive multi-task anti-noise learning (PMAL) framework and a progressive multi-task distilling (PMD) framework to solve the intra-class variation problem in FGVR due to image noise. The PMAL framework achieves high recognition accuracy by treating image denoising as an additional task in image recognition and progressively forcing a model to learn noise invariance. The PMD framework transfers the knowledge of the PMAL-trained model into the original backbone network, which produces a model with about the same recognition accuracy as the PMAL-trained model, but without any additional overheads over the original backbone network. Combining the two frameworks, we obtain models that significantly exceed previous state-of-the-art methods in recognition accuracy on two widely-used, standard FGVR datasets, namely Stanford Cars, and CompCars, as well as three additional surveillance image-based vehicle-type classification datasets, namely Beijing Institute of Technology (BIT)-Vehicle, Vehicle Type Image Data 2 (VTID2), and Vehicle Images Dataset for Make Model Recognition (VIDMMR), without any additional overheads over the original backbone networks. The source code is available at https://github.com/Dichao-Liu/Anti-noise_FGVR
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | CompCars | Resnet50 + PMAL | Accuracy | 99.1% | #1 of 7 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | TResnet-L + PMD | Accuracy | 97.3% | #1 of 83 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections